Nelson Project

Knowing Terrorism

This dataset contains the information of terrorism of the entire world from 1970 to 2016. Terrorism seems really far from us. We usually hear from the news. Only a very small portion of people in the world had experienced it. Therefore, this analysis would be using the dataset regarding on terrorism from Kaggle https://www.kaggle.com/START-UMD/gtd to present the audience some general information about terrorism.

1. Importing Dataset & Basic Handling of the Dataset

library(ggplot2) # Data visualization
library(readr) # CSV file I/O, e.g. the read_csv function
library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ tibble  3.1.0     ✓ dplyr   1.0.6
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ purrr   0.3.4     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(forcats)
library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

library(ggmap)

## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.

## Please cite ggmap if you use it! See citation("ggmap") for details.

library(ggthemes)


terrorism <- read_csv(file = 'globalterrorismdb_0718dist.csv',
                      col_types = cols(
                        iyear = col_integer(),
                        imonth = col_integer(),
                        iday = col_integer(),
                        nkill = col_double(),
                        nwound = col_double())
                      )

## Warning: 7113 parsing failures.
##  row             col           expected                                                                                                                                                                                     actual                             file
## 1687 ransomamtus     1/0/T/F/TRUE/FALSE 20000                                                                                                                                                                                      'globalterrorismdb_0718dist.csv'
## 1687 ransomnote      1/0/T/F/TRUE/FALSE A note, with a sticker of the Black Liberation Army, demanded $20,000 for the safe return of the bartender.  However, the perpetrators left the bartender alone and he was able to escape. 'globalterrorismdb_0718dist.csv'
## 1771 attacktype3     1/0/T/F/TRUE/FALSE 2                                                                                                                                                                                          'globalterrorismdb_0718dist.csv'
## 1771 attacktype3_txt 1/0/T/F/TRUE/FALSE Armed Assault                                                                                                                                                                              'globalterrorismdb_0718dist.csv'
## 3432 ransomnote      1/0/T/F/TRUE/FALSE Perpetrators demanded support by the Dutch in the UN for independence for the Moluccan Islands.                                                                                            'globalterrorismdb_0718dist.csv'
## .... ............... .................. .......................................................................................................................................................................................... ................................
## See problems(...) for more details.

head(terrorism)

Rename the columns so that they are readable

terrorism <- rename(terrorism, c('Year'='iyear','Month'='imonth','Day'='iday','Country'='country_txt','Region'='region_txt','AttackType'='attacktype1_txt','Target'='target1','Killed'='nkill','Wounded'='nwound','Summary'='summary','Group'='gname','Target_Type'='targtype1_txt','Weapon_Type'='weaptype1_txt','Motive'='motive'))
head(terrorism)

Remove the unnecessary columns

library(reshape2)

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
## 
##     smiths

ne_cols <- which(names(terrorism) %in%
                     c('Year','Month','Day','Country','Region','city','latitude','longitude','AttackType','Killed','Wounded','Target','Summary','Group','Target_Type','Weapon_type','Motive'))

terrorism <- terrorism[, ne_cols]

# terrorism <- data.frame(lapply(terrorism, as.factor))

head(terrorism)

# 
# summary(terrorism$Year)
# 
# head(terrorism)
# 
# print(fct_count(terrorism$Year, sort = FALSE))

# My personal theme for plotting
yuxwu22_398_theme <-  function() {
  theme_bw()  %+replace%
  theme(text = element_text(family="serif", face="bold", size=12))
  }

1. When & Where did the terrorism activity happen most frequently?

The first question is that when does the terrorism activity happen most frequently?

year_marginal <- terrorism %>%
  group_by(Year) %>%
  summarize(count = n())

year_p <- ggplot(data = year_marginal, aes(x= Year, y = count)) +
  geom_bar(stat = "identity") +
  geom_bar(data=subset(year_marginal, count==max(count)), aes(Year, count),
              fill="red", stat="identity") +
  scale_x_continuous(breaks = c(1970,1975,1980,1985,1990,1995,2000,2005,2010,2014,2017)) + 
  labs(title = "Number of Terrorist Activities Each Year",
       subtitle = "The terrorist activity happpended most frequently in 2014",
  caption = "Source: Global Terrorism Database",
  x = "Year",
  y = "Number of Terrorist Activities") +
  yuxwu22_398_theme() +
  theme(axis.text=element_text(size=13),
        title = element_text(size = 15))

year_p

ggsave(year_p, file = "when_1.png", dpi=300)

## Saving 8 x 5 in image

library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
## 
##     col_factor

max_year <- subset(terrorism, Year== 2014)

max_year$Month <- factor(max_year$Month)

max_year <- mutate(max_year,
                   Month = fct_recode(Month,
                                      "January" = "1",
                                      "February" = "2",
                                      "March" = "3",
                                      "April" = "4",
                                      "May" = "5",
                                      "June" = "6",
                                      "July" = "7",
                                      "August" = "8",
                                      "September" = "9",
                                      "October" = "10",
                                      "November" = "11",
                                      "December" = "12"))

max_year

month_marginal <- max_year %>%
  group_by(Month) %>%
  summarize(count = n()) %>%
  mutate(total = sum(count),  #  compute total number of observations
         proportion = count / total,  #  compute proportions
         percentage = proportion * 100,  #  compute percentages
         std_error = sqrt((proportion * (1 - proportion))/ total),  #  compute standard error of each proportion
         lower = proportion - 1.96 * std_error,  #  compute CI lower bound
         upper = proportion + 1.96 * std_error)  #  compute CI upper bound


# mfr_counts <- cereal %>%  #  Start with the cereal data.frame
#   group_by(mfr) %>%  #  group by the mfr variable
#   summarise(count = n()) %>%  #  summarize by calculating count of each manufacturer
#   mutate(total = sum(count),  #  compute total number of observations
#          proportion = count / total,  #  compute proportions
#          percentage = proportion * 100,  #  compute percentages
#          std_error = sqrt((proportion * (1 - proportion))/ total),  #  compute standard error of each proportion
#          lower = proportion - 1.96 * std_error,  #  compute CI lower bound
#          upper = proportion + 1.96 * std_error)  #  compute CI upper bound

month_marginal

month_p <- ggplot(data = month_marginal, aes(x= Month, y = count)) +
  geom_bar(stat = "identity") +
  geom_bar(data=subset(month_marginal, count==max(count)), aes(Month, count),
              fill="red", stat="identity") +
  # scale_x_discrete("Month", labels = c("1" = "January","2" = "February",
  # "3" = "March","4" = "April","5" = "May", "6" = "June", "7" = "July", "8" = "August", "9" = "September", "10" = "October", "11" = "November", "12" = "December")) +
  coord_flip() +
  scale_y_continuous(breaks = c(0, 1500)) +
  labs(title = "Year 2014",
  x = "Month",
  y = "Number of Terrorist Attacks") +
  yuxwu22_398_theme() +
  theme(axis.text=element_text(size=13),
        title = element_text(size = 15)) +
  geom_errorbar(aes(ymin = lower, ymax = upper))

month_p

ggsave(month_p, file = "when_2.png", dpi=300)

## Saving 8 x 5 in image

Additional computation for the bullet points

year_marginal[42:47,]

sum(year_marginal[42:47,2])

## [1] 76913

sum(year_marginal[42:47,2]) / sum(year_marginal[,2]) * 100

## [1] 42.33176

year_marginal[2,]

• 1971 is the year when the terrorism activity happen the least frequently with 471 recorded incident; 2014 is the year when the terrorism activity happen the most frequently with 16903 recorded incident.

• The number of recorded terrorism activities from 2012 to 2017 takes up approximately 42.3 percents of all recorded terrorism activities from 1970 to 2017.

• This dataset does not contain any record of terrorism activities in 1993.

The second question is that where did the terrorism activities happen most frequently?

library(httr)
GET("http://httpbin.org/delay/1", timeout(20))

## Response [http://httpbin.org/delay/1]
##   Date: 2021-05-15 12:10
##   Status: 200
##   Content-Type: application/json
##   Size: 417 B
## {
##   "args": {}, 
##   "data": "", 
##   "files": {}, 
##   "form": {}, 
##   "headers": {
##     "Accept": "application/json, text/xml, application/xml, */*", 
##     "Accept-Encoding": "deflate, gzip", 
##     "Host": "httpbin.org", 
##     "User-Agent": "libcurl/7.64.1 r-curl/4.3.1 httr/1.4.2", 
## ...

terrorism$longitude[is.na(terrorism$longitude)] <- 0
terrorism$latitude[is.na(terrorism$latitude)] <- 0

max(terrorism$longitude)

## [1] 179.3667

country_cond <- terrorism %>%
  filter(Country == 'China')

country_cond

min(country_cond$longitude)

## [1] 0

max(country_cond$longitude)

## [1] 126.6183

min(country_cond$latitude)

## [1] 0

max(country_cond$latitude)

## [1] 46.41216

us_bbox <- c(left = -180, bottom = -58, right = 180, top = 84)

map_base <- get_stamenmap(bbox = us_bbox,
                          maptype = "toner-lite",
                          zoom = 3)

## 54 tiles needed, this may take a while (try a smaller zoom).

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map_object <- ggmap(ggmap = map_base,
                    extent = "panel") +
  geom_point(data = terrorism, aes(x = longitude, y = latitude), colour = "red", size = 0.1) +
  labs(title = "Global Terrorist Activities from 1970 to 2017",
    caption = "Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.") +
  yuxwu22_398_theme() +
  theme(axis.title.x=element_blank(), axis.text.x=element_blank(),
                      axis.ticks.x=element_blank(),
        axis.title.y=element_blank(), axis.text.y=element_blank(),
                      axis.ticks.y=element_blank()) +
  theme(axis.text=element_text(size=13),
        title = element_text(size = 15))
  

map_object

## Warning: Removed 1 rows containing missing values (geom_point).

ggsave(map_object, file = "where_1.png", dpi=300)

## Saving 8 x 5 in image

## Warning: Removed 1 rows containing missing values (geom_point).

region_cond <- subset(terrorism, Region == "Middle East & North Africa" | Region == "South Asia" | Region =="South America")

region_cond <- region_cond %>%
  group_by(Region) %>%
  summarize(count = n())

region_p <- ggplot(data = region_cond, aes(x= reorder(Region,+count), y = count)) +
  geom_bar(stat = "identity") +
  geom_bar(data=subset(region_cond, count==max(count)), aes(Region, count),
              fill="red", stat="identity") +
  labs(y = "Number of Terriorist Activities",
       x = "Countries",
       title = "Regions that have the most terrorist attacks",
       caption = "Source: Global Terror Database") +
  scale_y_continuous(breaks = c(0, 20000,50000), position = "left") +
  yuxwu22_398_theme() +
  coord_flip() +
  theme(axis.title.y = element_blank(),
    axis.text=element_text(size=13),
        title = element_text(size = 15))

region_p

ggsave(region_p, file = "where_2.png", dpi=300)

## Saving 8 x 5 in image

Third question, what is the trend of the terrorism activities by region over time? (time series)

# region_marg <- terrorism %>%
#   group_by(Region) %>%
#   summarize(Region_Count = n())
# 
year_marginal <- terrorism %>%
  group_by(Year) %>%
  summarize(Year_Total = n())
# 
# region_marg

source("http://pcwww.liv.ac.uk/~william/R/crosstab.r")

year_no_total <- crosstab(terrorism, row.vars = "Year", col.vars = "Region", type = "f", style = "long", addmargins = FALSE)

# year_no_total$Year_Total <- rowSums( year_no_total )

year_total <- year_no_total$table

year_total <- as.data.frame.matrix(year_total) 

year_total$Year <- year_marginal$Year

# year_total <- cbind(year_total, year_marginal$Year)

year_total 

Asia <- data.frame(year_total["Central Asia"] +year_total["East Asia"] + year_total["South Asia"] + year_total["Southeast Asia"])

Asia <- rename(Asia, "Asia" = 'Central.Asia')

year_total <- cbind(year_total, Asia$Asia)

America_Carribean <- data.frame(year_total["Central America & Caribbean"] +year_total["North America"] + year_total["South America"])

America_Carribean <- rename(America_Carribean, "America_Carribean" = 'Central.America...Caribbean')

year_total <- cbind(year_total, America_Carribean$America_Carribean)

Europe <- data.frame(year_total["Eastern Europe"] +year_total["Western Europe"])

Europe <- rename(Europe, "Europe" = 'Eastern.Europe')

year_total <- cbind(year_total, Europe$Europe)

ME_Africa <- data.frame(year_total["Middle East & North Africa"] +year_total["Sub-Saharan Africa"])

ME_Africa <- rename(ME_Africa, "ME_Africa" = 'Middle.East...North.Africa')

year_total <- cbind(year_total, ME_Africa$ME_Africa)

year_total

# trend_data <- rbind(year_no_total, year_marginal["Year_Total"])
# 
# trend_data
country <- c("Australasia & Oceania", "Central America & Caribbean", "Central Asia", "East Asia", "Eastern Europe", "Middle East & North Africa", "North America", "South America", "South Asia", "Southeast Asia", "Sub-Saharan Africa", "Western Europe" )

# The palette with black:
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#950EE7",
          "#DAF7A6", "#08F3F9", "#36F215")

# region <- colnames(year_total)

library(jcolors)

trend <- ggplot(data = year_total, aes(x= year_total[,13])) +
  geom_line(aes(y = year_total[,1], color = country[1]),size=1) + 
  geom_line(aes(y = year_total[,14], color = "Asia"),size=1) +
  geom_line(aes(y = year_total[,15], color = "America & Carribean"),size=1) +
  geom_line(aes(y = year_total[,16], color = "Europe"),size=1) +
  geom_line(aes(y = year_total[,17], color = "Middle East & Africa"),size=1) +
  # geom_line(aes(y = year_total[,5], color = country[5]),size=1) +
  # geom_line(aes(y = year_total[,6], color = country[6]),size=1) +
  # geom_line(aes(y = year_total[,7], color = country[7]),size=1) +
  # geom_line(aes(y = year_total[,8], color = country[8]),size=1) +
  # geom_line(aes(y = year_total[,9], color = country[9]),size=1) +
  # geom_line(aes(y = year_total[,10], color = country[10]),size=1) +
  # geom_line(aes(y = year_total[,11], color = country[11]),size=1) +
  # geom_line(aes(y = year_total[,12], color = country[12]),size=1) +
  scale_x_continuous(breaks = c(1970,1975,1980,1985,1990,1995,2000,2005,2010,2017)) +
  labs(title = "Trend of Terrorist Activities",
       subtitle = "Terrorist activities increase tremendously in Middle East & North Africa for last 10 years",
  caption = "Source: Global Terrorism Database",
  x = "Year",
  y = "Number of Terrorist Activities",
  colour = "Region") +
  yuxwu22_398_theme() +
  scale_color_jcolors(palette = "pal8") +
  theme(axis.text=element_text(size=13),
        title = element_text(size = 15),
        legend.text = element_text(size = 13))
  
  

trend

ggsave(trend, file = "trend_country.png", dpi=300)

## Saving 10 x 5 in image

2. Which type of Terrorism Activity occur most often?

region_type <- crosstab(terrorism, row.vars = "Region", col.vars = "AttackType", type = "f", style = "long", addmargins = FALSE)

region_type <- region_type$table

region_type <- as.data.frame.matrix(region_type)

region_type

attack_marginal <- terrorism %>%
  group_by(AttackType) %>%
  summarize(Total = n())

library(jcolors)

# atc_type <- ggplot(data = terrorism, aes(x= AttackType)) +
#          geom_bar(aes(fill = Region)) +
#           coord_flip() +
#       labs(title = "Types of Terrorism Activity",
#         subtitle = "Bombing is still the 'most popular' choice",
#         caption = "Source: Global Terrorism Database",
#          y = "Number of Terrorism Activity",
#        colour = "Region") +
#       theme(axis.title.y=element_blank(), axis.title.x=element_blank()) +
#       yuxwu22_398_theme() +
#       scale_color_jcolors(palette = "pal8")
# 
# atc_type

fct_count(terrorism$Region)

terrorism <- terrorism %>%
  mutate(Continent = fct_collapse(Region, "Asia" = c("Central Asia", "East Asia","South Asia", "Southeast Asia"),
                                   "America & Carribean" = c("Central America & Caribbean", "North America", "South America"),
                                   "Europe" = c("Eastern Europe", "Western Europe"),
                                   "Middle East & Africa" = c("Middle East & North Africa", "Sub-Saharan Africa")))

terrorism$Continent <- fct_relevel(terrorism$Continent, c("America & Carribean", "Asia", "Australasia & Oceania","Europe", "Middle East & Africa")) 

atc_type_subset <- subset(terrorism, Group == "Taliban" | Group == "Islamic State of Iraq and the Levant (ISIL)" | Group == "Al-Shabaab" | Group == "Shining Path (SL)" | Group == "Farabundo Marti National Liberation Front (FMLN)")

atc_type_subset <- within(atc_type_subset, 
                   AttackType <- factor(AttackType, 
                                      levels=names(sort(table(AttackType), 
                                                        increasing=TRUE))))

atc_type <-
  ggplot(data = atc_type_subset, aes(x = AttackType, fill = Group)) +
  geom_bar() +
  labs(
    title = "Types of Terrorist Activity",
    subtitle = "Bombing is still the 'most popular' choice",
    caption = "Source: Global Terrorism Database",
    y = "Number of Terrorist Activity",
    fill = "Region"
  ) +
  yuxwu22_398_theme() +
  theme(axis.title.y = element_blank()) +
  coord_flip() +
  theme(axis.text=element_text(face = "bold", size=13),
        title = element_text(size = 15),
        legend.text = element_text(size = 13))
  



atc_type <- atc_type + scale_fill_jcolors(palette = "pal8")

atc_type

ggsave(atc_type, file = "attack_type.png", dpi=300)

## Saving 14 x 5 in image

3. Terrorist group & Activate Year?

# region_marg <- terrorism %>%
#   group_by(Region) %>%
#   summarize(Region_Count = n())
# 
group_marginal <- terrorism %>%
  group_by(Group) %>%
  summarize(Total = n())
# 
# region_marg

source("http://pcwww.liv.ac.uk/~william/R/crosstab.r")

group_no_total <- crosstab(terrorism, row.vars = "Year", col.vars = "Group", type = "f", style = "long", addmargins = FALSE)

# year_no_total$Year_Total <- rowSums( year_no_total )

group_total <- group_no_total$table

class(group_total)

## [1] "table"

group_total <- as.data.frame.matrix(group_total)

group_total <- group_total[c("Taliban", "Shining Path (SL)", "Islamic State of Iraq and the Levant (ISIL)", "Farabundo Marti National Liberation Front (FMLN)", "Al-Shabaab","Irish Republican Army (IRA)", "Revolutionary Armed Forces of Colombia (FARC)", "New People's Army (NPA)", "Kurdistan Workers' Party (PKK)", "Boko Haram", "Basque Fatherland and Freedom (ETA)")]

group_names <- c("Taliban", "Shining Path (SL)", "Islamic State of Iraq and the Levant (ISIL)", "Farabundo Marti National Liberation Front (FMLN)", "Al-Shabaab","Irish Republican Army (IRA)", "Revolutionary Armed Forces of Colombia (FARC)", "New People's Army (NPA)", "Kurdistan Workers' Party (PKK)", "Boko Haram", "Basque Fatherland and Freedom (ETA)")

group_total$Year <- year_marginal$Year


# trend_data <- rbind(year_no_total, year_marginal["Year_Total"])
# 
# trend_data

# country <- c("Australasia & Oceania", "Central America & Caribbean", "Central Asia", "East Asia", "Eastern Europe", "Middle East & North Africa", "North America", "South America", "South Asia", "Southeast Asia", "Sub-Saharan Africa", "Western Europe" )

# The palette with black:
# cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
#           "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#950EE7",
#           "#DAF7A6", "#08F3F9", "#36F215")

# region <- colnames(year_total)
# 
# library(jcolors)
# 
trend_group <- ggplot(data = group_total, aes(x= group_total[,12])) +
  geom_line(aes(y = group_total[,1], color = group_names[1]),size=1) +
  geom_line(aes(y = group_total[,2], color = group_names[2]),size=1) +
  geom_line(aes(y = group_total[,3], color = group_names[3]),size=1) +
  geom_line(aes(y = group_total[,4], color = group_names[4]),size=1) +
  geom_line(aes(y = group_total[,5], color = group_names[5]),size=1) +
  # geom_line(aes(y = group_total[,6], color = group_names[6]),size=1) +
  # geom_line(aes(y = group_total[,7], color = group_names[7]),size=1) +
  # geom_line(aes(y = group_total[,8], color = group_names[8]),size=1) +
  # geom_line(aes(y = group_total[,9], color = group_names[9]),size=1) +
  # geom_line(aes(y = group_total[,10], color = group_names[10]),size=1) +
  # geom_line(aes(y = group_total[,11], color = group_names[11]),size=1) +
  scale_x_continuous(breaks = c(1970,1975,1980,1985,1990,1995,2000,2005,2010,2017)) +
  labs(title = "Trend of Terrorist Groups Activities",
       subtitle = "Top two groups for the last five years are Taliban and ISIL",
  caption = "Source: Global Terrorism Database",
  x = "Year",
  y = "Number of Terrorist Activities",
  colour = "Terrorist Group") +
  yuxwu22_398_theme() +
  scale_color_jcolors(palette = "pal8") +
  theme(axis.text=element_text(size=13),
        title = element_text(size = 15),
        legend.text = element_text(size = 13))



trend_group

ggsave(trend_group, file = "terrorist_trend.png", dpi=300)

## Saving 10 x 5 in image

Second question, where did each group usually activate?

us_bbox <- c(left = -180, bottom = -58, right = 180, top = 84)

map_base <- get_stamenmap(bbox = us_bbox,
                          maptype = "toner-lite",
                          zoom = 3)

## 54 tiles needed, this may take a while (try a smaller zoom).

map_object_2 <- ggmap(ggmap = map_base,
                    extent = "panel") +
  geom_point(data= subset(terrorism, Group == group_names[1]), aes(x = longitude, y = latitude, color = group_names[1]), size = 2) +
  geom_point(data= subset(terrorism, Group == group_names[2]), aes(x = longitude, y = latitude, color = group_names[2]), size = 2) +
  geom_point(data= subset(terrorism, Group == group_names[3]), aes(x = longitude, y = latitude, color = group_names[3]), size = 2) +
  geom_point(data= subset(terrorism, Group == group_names[4]), aes(x = longitude, y = latitude, color = group_names[4]), size = 2) +
  geom_point(data= subset(terrorism, Group == group_names[5]), aes(x = longitude, y = latitude, color = group_names[5]), size = 2) +
  labs(title = "Regional Activities of Top Five Terrorist Groups",
    caption = "Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.",
    color = "Terrorist Groups") +
  yuxwu22_398_theme() +
  theme(axis.title.x=element_blank(), axis.text.x=element_blank(),
                      axis.ticks.x=element_blank(),
        axis.title.y=element_blank(), axis.text.y=element_blank(),
                      axis.ticks.y=element_blank()) + scale_color_jcolors(palette = "pal8") +
  theme(axis.text=element_text(size=13),
        title = element_text(size = 15),
        legend.text = element_text(size = 13))
  

map_object_2

ggsave(map_object_2, file = "group_location.png", dpi=300)

## Saving 12 x 6 in image

group_cond <- subset(terrorism, Group == group_names[1] | Group == group_names[2] | Group == group_names[3])

group_cond <- group_cond %>%
  group_by(Group) %>%
  summarize(count = n())

group_p <- ggplot(data = group_cond, aes(x= reorder(Group,-count), y = count)) +
  geom_bar(stat = "identity") +
  geom_bar(data=subset(group_cond, count==max(count)), aes(Group, count),
              fill="red", stat="identity") +
  labs(y = "Number of Terriorism Activities") +
  scale_y_continuous(breaks = c(0, 7000), position = "left") +
  yuxwu22_398_theme() +
  theme(axis.title.x=element_blank(),
        axis.text=element_text(size=12)) +
  theme(axis.text=element_text(size=13),
        title = element_text(size = 15))

group_p

ggsave(group_p, file = "group_location_2.png", dpi=300)

## Saving 9 x 5 in image

4. Attack vs. Killed and Target?

terrorism$Killed[is.na(terrorism$Killed)] <- 0

terrorism

kill_cond <- subset(terrorism, Country == "Iraq" | Country == "Pakistan" | Country == "Afghanistan" | Country == "India"| Country == "Colombia"| Country == "Philippines" | Country == "Peru")

att <- kill_cond  %>%
  group_by(Country) %>%
  summarize(Acc_Total = n())

att

kill <- kill_cond  %>%
  group_by(Country) %>%
  summarise(
    Kill_Total = sum(Killed)
  )

kill$Acc_Total <- att$Acc_Total

kill <- melt(kill)

## Using Country as id variables

kill

att_vs_killed <- ggplot(data = kill, aes(x= reorder(Country, +value), y = value, fill = variable)) +
  geom_bar(stat = "identity", position = "dodge") +
  coord_flip() +
  labs(fill = "a",
       title = "Attacks vs. Killed") +
  scale_y_continuous(breaks = c(0, 20000,50000), position = "left") +
  yuxwu22_398_theme() +
  theme(axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        legend.title = element_blank(),
        axis.text=element_text(size=8)) +
  scale_fill_discrete(labels = c("Number of People Got Killed", "Number of Terrorist Attacks")) +
  theme(axis.text=element_text(size=13),
        title = element_text(size = 15),
        legend.position = "bottom")

att_vs_killed

ggsave(att_vs_killed, file = "casulties.png", dpi=300)

## Saving 7 x 5 in image

Target?

library(randomcoloR)
library(cowplot)

## 
## Attaching package: 'cowplot'

## The following object is masked from 'package:ggthemes':
## 
##     theme_map

## The following object is masked from 'package:ggmap':
## 
##     theme_nothing

n <- 50
palette <- distinctColorPalette(n)
pal <- randomColor(count=50)




target <- terrorism  %>%
  group_by(Target_Type) %>%
  summarize(Total = n())

target <- melt(target)

## Using Target_Type as id variables

terrorism <- terrorism %>%
  mutate(less_target_type = fct_collapse(Target_Type, "Non-government/Non-military Related" = c("Private Citizens & Property", "Tourists","Business", "Educational Institution", "Journalists & Media", "NGO", "Maritime", "Abortion Related", "Religious Figures/Institutions"),
                                   "Government Related" = c("Government (Diplomatic)", "Government (General)", "Violent Political Party"),
                                   "Infrastructures" = c("Airports & Aircraft", "Food or Water Supply", "Telecommunication", "Transportation", "Utilities")))

terrorism

# terrorism$Continent <- fct_relevel(terrorism$Continent, c("America & Carribean", "Asia", "Australasia & Oceania","Europe", "Middle East & Africa")) 

target_pie <- ggplot(terrorism, aes(x = less_target_type)) + 
  geom_bar(aes(fill = less_target_type)) +
  aes(x = factor("")) +
  labs(fill = "Target Types") + 
  yuxwu22_398_theme() +
  theme(axis.ticks=element_blank(),
          axis.title=element_blank(),
          axis.text.y=element_blank()
          ) +
  guides(fill=guide_legend(override.aes=list(colour=NA))) +
  coord_polar(theta = "y") +
  scale_fill_jcolors(palette = "pal8") +
  theme(
        axis.text=element_text(size=13),
        title = element_text(size = 15),
        legend.position="top",
        legend.text = element_text(size = 13),
        legend.title = element_blank())

target_pie

ggsave(target_pie, file = "target_1.png", dpi=300)

## Saving 9 x 5 in image

terrorism_subset <- terrorism %>%
  filter(less_target_type == "Non-government/Non-military Related")

target_pie_2 <- ggplot(terrorism_subset, aes(x = Target_Type)) + 
  geom_bar(aes(fill = Target_Type)) +
  aes(x = factor("")) +
  labs(fill = "Target Types",
       caption = "Source: Global Terrorism Database") + 
  yuxwu22_398_theme() +
  theme(axis.ticks=element_blank(),
          axis.title=element_blank(),
          axis.text.y=element_blank()
          ) +
  guides(fill=guide_legend(override.aes=list(colour=NA))) +
  coord_polar(theta = "y") +
  scale_fill_brewer(palette = "Set3") +
  theme(axis.text=element_text(size=13),
        title = element_text(size = 15),
        legend.position = "bottom",
        legend.text = element_text(size = 13),
        legend.title = element_blank())

target_pie_2

ggsave(target_pie_2, file = "target_2.png", dpi=300)

## Saving 10 x 5 in image

ggplot() + 
  borders("world", colour = "gray50", fill = "gray50") +
  geom_point(data = terrorism, aes(x = longitude, y = latitude), colour = "red", size = 0.1) +
  labs(title = "Global Terrorist Activities from 1970 to 2017",
    caption = "Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.") +
  yuxwu22_398_theme() +
  theme(axis.title.x=element_blank(), axis.text.x=element_blank(),
                      axis.ticks.x=element_blank(),
        axis.title.y=element_blank(), axis.text.y=element_blank(),
                      axis.ticks.y=element_blank()) +
  theme(axis.text=element_text(size=13),
        title = element_text(size = 15))